Exploring secure and private data aggregation techniques for the internet of things: a comprehensive review (2025)

An Overview of Privacy-Preserving Data Aggregation in IoT

IOSR Journals , 2019

Internet of Things (IoT) is a system of interrelated connected physical objects that are accessible through the internet. Internet of Things offers the most flexibility and convenience in our daily applications as the IoT devices can improve productivity, accuracy and financial benefit in addition to reduced human intrusion. Security, privacy and communication overhead Problems are also arising in IoT. To address this problem, many privacy-preserving data aggregation schemes have been proposed in the past years. Privacy-preserving data aggregation is one application in IoT. Privacy-preserving data aggregation is a main building block that can protect user's privacy. In this paper, we present an overview of privacy-preserving data aggregation scheme for IoT to preserve privacy and to reduce communication overhead. There are many Privacy-Preserving Data Aggregation (PPDA) approaches have been proposed to ensure data privacy during data aggregation in resource-constrained sensor nodes. We provide an overview and analysis of the state of the art PPDA approaches in this paper. We have evaluated the most recent approachesand provide in-depth analysis of the minute steps involved in these approaches. In addition, this overview gives very analysis of each mathematical operation involve in different PPDA schemes. This study will help the researchers to design energy efficient and computationally feasible solution to ensure user's privacy in IoT applications.

Privacy-Preserving Data Aggregation in Internet of Things (Iot): An Overview

International Journal of Advanced Research in Computer Science, 2020

IoT devices enhance efficiency, accuracy and economic advantages along with less involvement of human resources, thus our different daily applications have become more flexible and convenient. But, in IoT we have many security and privacy challenges emerging on regular basis. Earlier this issue has been addressed by introduction of many approaches for achieving privacy-preserving in data aggregation process. In this aspect this paper presents an outline of IoT-oriented approach for achieving privacy preservation together with minimizing communication overhead. This paper reviews the latest Privacy Preserving Data Aggregation (PPDA) techniques along with their comparative analysis. Latest techniques are investigated here to give a detail analysis of the each and every step of these techniques. In addition, every mathematical operation used in several PPDA schemes is analyzed here. Also current study will be advantageous to researchers in designing solutions in terms of energy efficiency and computational feasibility for ensuring user privacy in different IoT application.

Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

2021

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers...

PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

Journal of Ambient Intelligence and Humanized Computing

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preserving. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been caused a big challenge in this field. To improve this trafe-off efficiency rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages homomorphic paillier encryption, K-means, a one-way hash chain, and the Chinese Remainder Theorem. We have found that the proposed privacy-preserving architecture achieves a more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted 8.096% improvement over LPDA and 6.508% over PPIOT.

SPECIAL SECTION ON SECURITY AND PRIVACY IN APPLICATIONS AND SERVICES FOR FUTURE INTERNET OF THINGS A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices' data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices' data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.

A Novel Secure and Efficient Data Aggregation Scheme for IoT

IEEE Internet of Things Journal, 2019

We define the following problem termed n × 1-outof-n oblivious transfer (n × 1-out-of-n OT for short): in a system with one server and n clients, how to securely and efficiently assign n secrets to n clients by the server, with each client getting a unique secret from the server, and the server and clients remain unknown of how the secrets are distributed? This is a novel problem that is fundamentally different than 1-out-of-n OT repeated n times, and is different than k-out-of-n OT as well. Nevertheless, the proposed OT has many practical applications such as privacy-preserving data aggregation in smart grids. It can also be employed to design crypto protocols for anonymous communications and group signatures. In this paper, we propose the first algorithm to efficiently and effectively implement the n × 1-out-of-n OT. We construct hidden permutation circuits to obliviously assign n secrets to n clients by the server within O(lg(n)) time. A rigorous theoretical analysis is also carried out to investigate the security strength and performance of the protocol.

Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review

Privacy problems are lethal and getting more attention than any other issue with the notion of the Internet of Things (IoT). Since IoT has many application areas including smart home, smart grids, smart healthcare system, smart and intelligent transportation and many more. Most of these applications are fueled by the resource-constrained sensor network, such as Smart healthcare system is powered by Wireless Body Area Network (WBAN) and Smart home and weather monitoring systems are fueled by Wireless Sensor Networks (WSN). In the mentioned application areas sensor node life is a very important aspect of these technologies as it explicitly effects the network life and performance. Data aggregation techniques are used to increase sensor node life by decreasing communication overhead. However, when the data is aggregated at intermediate nodes to reduce communication overhead, data privacy problems becomes more vulnerable. Different Privacy-Preserving Data Aggregation (PPDA) techniques have been proposed to ensure data privacy during data aggregation in resource-constrained sensor nodes. We provide a review and comparative analysis of the state of the art PPDA techniques in this paper. The comparative analysis is based on Computation Cost, Communication overhead, Privacy Level, resistance against malicious aggregator, sensor node life and energy consumption by the sensor node. We have studied the most recent techniques and provide in-depth analysis of the minute steps involved in these techniques. To the best of our knowledge, this survey is the most recent and comprehensive study of PPDA techniques.

A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

IEEE Access, 2017

Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices' data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices' data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.

A Lightweighted Secure Scheme for Data Aggregation in Large-Scale IoT-Based Smart Grids

Journal of Computer and Knowledge Engineering, 2023

With the emergence of IoT devices, data aggregation in the area of smart grids can be implemented based on IoT networks. However, the communication and computation resources of IoT devices are limited so it is not possible to apply conventional Internet protocols directly. On the other hand, gathering data from smart meters in the advanced metering infrastructure faces challenges such as privacy-preserving and heavy-loaded authentication and aggregation schemes. In this paper, we propose an improved lightweight, secure, and privacypreserving scheme for aggregating data of smart meters in largescale IoT-based smart grids. The proposed scheme adopts lightweight operations of cryptography such as exclusive-OR, hash, and concatenation functions. In comparison with the schemes in the literature, the analysis and simulation results show that the proposed scheme satisfies the same security levels, while at the same time burdens lower computation and communication overheads. This observation makes the proposed scheme more suitable to be employed in large-scale and IoT-based smart grids for data aggregation.

An Authentication-Based Secure Data Aggregation Method in Internet of Things

Journal of Grid Computing

phase is to improve the security of inter-cluster communications using an authentication protocol. In this way, the cluster heads are authenticated before sending information to prevent malicious nodes in the network. The proposed method is also simulated using NS2 software. The results showed that the proposed method has improved in terms of energy consumption, end-to-end delay, flexibility, packet delivery rate, and the number of alive nodes compared to other methods.